Abstract P666: Covid-19 Geographic Distribution and Stroke Code Activation Within San Diego County

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Emily Perrinez ◽  
Robin Calara ◽  
Paige Schoenheit-Scott ◽  
Amelia Kenner Brininger ◽  
Lindsay L Olson-Mack ◽  
...  
Keyword(s):  
Emergency Medical Services ◽  
San Diego ◽  
San Diego County ◽  
Infection Rates ◽  
Activation Rate ◽  
Cumulative Rate ◽  
Activation Data ◽  
Private Transportation ◽  
Activation Rates ◽  
Zip Code

Introduction: In the early months of the COVID-19 pandemic, decreased numbers of stroke code activations were reported nationwide. In San Diego County, a diverse region that borders Mexico with over 4500 square miles and population 3.3 million, trends in COVID-19 cases varied geographically. We saw an overall decrease in stroke cases across our systems and aimed to better understand if high COVID infection rates in subregions affected stroke code activations. Methods: Stroke code activation data from 15 Stroke Receiving Centers were matched with COVID-19 case rates by patient home zip code. Patients arriving via emergency medical services (EMS) or private transportation were included. Patients with home zip codes outside of San Diego County were excluded. Data represented the cumulative rate of stroke codes and COVID-19 cases per 100,000 population per zip code for the period of March 1 through June 30, 2020. Results: We counted 1,927 stroke code activations across 106 zip codes in San Diego County. The average stroke code activation rate was 58.4 per 100,000 (range: 0-310.6) The median stroke code activation rate was 55.95 (IQR=32.1-73.1) per 100,000 population. The median COVID rate per zip code was 244.9 (IQR=177-448.4) per 100,000 population. There were 958 (49.7%) non-stroke diagnoses, 576 (29.9%) AIS, 272 (14.1%) TIA, 104 (5.4%) ICH and 17 (.9%) SAH. We did not identify a correlation between stroke code activation rates and COVID rates across zip codes (r=.17, p=.09, 95% CI(-.02, .35)). Conclusions: Across a large and diverse single-county region, no correlation was found between COVID positivity rate per zip code and stroke code activations. We found no decreases in stroke code activations in areas with high COVID rates.

Impact of the San Diego county firestorm on emergency medical services

2004 ◽  
Vol 44 (4) ◽  
pp. S102-S103
Author(s):  
G.M. Vilke ◽  
P.A. Murrin ◽  
A. Marcotte ◽  
R.L. Upledger
Keyword(s):  
Emergency Medical Services ◽  
San Diego ◽  
San Diego County ◽  
Medical Services ◽  
Emergency Medical

Abstract WP270: Using County Stroke Receiving System Data to Identify Areas in Need of Public Health Intervention

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Lindsay Olson-Mack ◽  
Amelia Kenner-Brininger ◽  
Diane Royer ◽  
Bruce Haynes ◽  
Thomas M Hemmen
Keyword(s):  
Blood Pressure ◽  
High Blood Pressure ◽  
San Diego ◽  
Regional Difference ◽  
Regional Distribution ◽  
Community Outreach ◽  
San Diego County ◽  
Outreach Program ◽  
Stroke Incidence ◽  
Zip Code

Background: Patients who alert the Emergency Medical Services (EMS) system arrive to the hospital sooner and receive faster and more appropriate care after stroke. We investigated the regional distribution of EMS alerts in stroke patients throughout San Diego County. Our aim is to document regional difference that may provide opportunities for improvement in community outreach. Methods: We included all patients with principle discharge diagnosis of stroke in the San Diego County Stroke Registry from 01/2015 to 12/2015. We analyzed stroke incidence by ZIP code in cases per 100,000 and use of EMS in percent of all stroke discharges by ZIP code. Each ZIP code was characterized by race/ethnicity, age, use of prescriptions for high blood pressure, diabetes and smoking from ESRI Community Analyst. ZIP codes with fewer than 10 stroke cases were excluded. We used Pearson correlation with significance level of p<0.05. Results: In total we found 5,302 stroke discharges, 4,163 (78.5%) matched to one of 77 ZIP codes in San Diego County. The rate of stroke incidence ranged from 42.9 to 263.9 cases per 100,000 residents. Frequency of EMS use ranged from 26.3% to 83.3%. Rate of stroke was positively correlated with older age, use of prescription drugs for high blood pressure and diabetes. EMS use was higher in ZIP codes with increased smoking (p=0.02). No other variable correlated with EMS use within ZIP codes. Conclusion: The rate of EMS alert after stroke varies considerably across our region. We did not identify a robust predictor for higher EMS use within a ZIP code. Our data suggests that further studies are needed to best understand the variance in EMS use. The regional difference, however, justify a targeted community outreach program to improve EMS utilization after stroke.

Impact of the San Diego County Firestorm on Emergency Medical Services

2006 ◽  
Vol 21 (5) ◽  
pp. 353-358 ◽  
Author(s):  
Gary M. Vilke ◽  
Alan M. Smith ◽  
Barbara M. Stepanski ◽  
Leslie Upledger Ray ◽  
Patricia A. Murrin ◽  
...  
Keyword(s):  
Emergency Medical Services ◽  
San Diego ◽  
Life Support ◽  
Advanced Life Support ◽  
San Diego County ◽  
Medical Services ◽  
Eye Injuries ◽  
Emergency Medical ◽  
The Impact ◽  
Respiratory Complaints

AbstractBackground:In October 2003, San Diego County, California, USA, experienced the worst firestormin recent history. During the firestorm, public health leaders implemented multiple initiatives to reduce its impact on community health using health updates and news briefings. This study assessed the impact of patients with fire-related complaints on the emergency medical services (EMS) system during and after the firestorm.Methods:A retrospective review of a prehospital database was performed for all patients who were evaluated by advanced life support (ALS) ambulance personnel after calling the 9-1-1 emergency phone system for direct, fire related complaints from 19 October 2003 through 30 November 2003 in San Diego County. The study location has an urban, suburban, rural, and remote resident population of approximately three million and covers 4,300 square miles (2,050 km2). The prehospital patient database was searched for all patients with a complaint that was related directly to the fires. Charts were abstracted for data, including demographics, medical issues, treatments, and disposition status.Results:During the firestorm, fire consumed >380,000 acres (>938,980 hectares), including 2,454 residences and 785 outbuildings, and resulted in a total of 16 fatalities. Advanced life support providers evaluated 138 patients for fire related complaints. The majority of calls were for acute respiratory complaints. Other complaints included burns, trauma associated with evacuation or firefighting, eye injuries, and dehydration. A total of 78% of the injuries were mild. Twenty percent of the victims were firefighters, most with respiratory complaints, eye injuries, or injuries related to trauma. A total of 76% of the patients were transported to the hospital, while 10% signed out against medical advice.Conclusion:Although the firestorm had the potential to significantly impact EMS, pre-emptive actions resulted in minimal impact to emergency departments and the prehospital system. However, during the event, therewere a number of lessons learned that can be used in future events.

scholarly journals Analyzing Socioeconomic Factors and Health Disparity of COVID-19 Spatiotemporal Spread Patterns at Neighborhood Levels in San Diego County

2021 ◽  
Author(s):  
Ming-Hsiang Tsou ◽  
Jian Xu ◽  
Chii-Dean Lin ◽  
Morgan Daniels ◽  
Jessica Embury ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Rural Areas ◽  
Health Disparity ◽  
Regression Models ◽  
San Diego ◽  
Negative Relationship ◽  
San Diego County ◽  
Economic Factors ◽  
Stage 4 ◽  
Zip Code

AbstractThis study analyzed spatiotemporal spread patterns of COVID-19 confirmed cases at the zip code level in the County of San Diego and compared them to neighborhood social and economic factors. We used correlation analysis, regression models, and geographic weighted regression to identify important factors and spatial patterns. We broke down the temporal confirmed case patterns into four stages from 1 April 2020 to 31 December 2020. The COVID-19 outbreak hotspots in San Diego County are South Bay, El Cajon, Escondido, and rural areas. The spatial patterns among different stages may represent fundamental health disparity issues in neighborhoods. We also identified important variables with strong positive or negative correlations in these categories: ethnic groups, languages, economics, and education. The highest association variables were Pop5andOlderSpanish (Spanish-speaking) in Stage 4 (0.79) and Pop25OlderLess9grade (Less than 9th grade education) in Stage 4 (0.79). We also observed a clear pattern that regions with more well-educated people have negative associations with COVID-19. Additionally, our OLS regression models suggested that more affluent populations have a negative relationship with COVID-19 cases. Therefore, the COVID-19 outbreak is not only a medical disease but a social inequality and health disparity problem.

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